There has been a great deal of discussion surrounding the current artificial intelligence (AI) boom, as well as the potential for a bust reminiscent of the end of the dot com era. AI continues to predominate venture capital (VC) investment, with recently reporting that 鈥淰C investors continued to double down on AI in Q3鈥25, with companies developing AI models and platforms attracting many of the largest funding rounds of the quarter.鈥 And this is showing no signs of slowing down.
While experts can debate whether we are in an AI bubble that could burst, unlike the boom cycles we have experienced in the past, this time, investors are becoming more selective. AI startup formation will no doubt continue its surge as we move into 2026, but funding will become even more concentrated among those companies that can demonstrate a real product-market fit and a credible plan for legal rights and regulatory compliance.
Below are three trends that we can expect to define the AI sector next year.
A Continued Shift in Investor Focus
The capital that is flowing into AI companies at historic levels is not doing so evenly, and this will continue into next year. The bulk of funding is being funneled to fewer, more mature companies. Savvy investors are no longer looking to simply finance experimentation, and most late-stage funding is going to a smaller number of well-capitalized market leaders, leaving many earlier stage companies operating under structural pressure.
As we recently discussed at the 2025 TED AI conference, this has led to 鈥渁 tale of two worlds,鈥 with many earlier stage AI startups facing the challenge today of scaling revenue or retaining engineers amid intense talent poaching. So, the earlier stage startups in the AI space can no longer rely simply on their technical potential. They must prove they can trigger and sustain hyper-growth in revenue while retaining key engineering talent in a market where the larger companies can lure away top talent with compensation packages startups cannot match.
Investors are also prioritizing those companies they feel can best withstand the legal and regulatory scrutiny that is ahead. This means founders must build an infrastructure from the start that can support not only technical growth, but also legal and regulatory durability long term. That means having the legal rights to the data that your models are training on, and their outputs. It also means complying with a complex web of interlocking regulatory structures that can be national, supra-national, regional, or even local. While regulations have jurisdictional borders, AI tools can be accessed anywhere in the world.
A Shakeout Among Horizontal AI Startups
Next year, expect there to be a shakeout among horizontal AI startups that lack vertical specialization or agentic capabilities. Investors want to see companies solving domain-specific problems with proprietary data and actionable outputs. We are moving past the era of generic AI platforms to one of targeted, high-value solutions in regulated or operationally complex sectors. Capital will flow to those AI companies that own a problem, not just a model. The era of undifferentiated AI platforms is coming to an end.
A Surge in M&A
At the same time as all of this is taking place, the capital markets are evolving in parallel. We have started to see the IPO window cautiously reopen, but public market entry is not likely to be the first source of liquidity for these VC-backed companies. Instead, 2026 will likely bring a rise in strategic mergers and acquisitions (M&A) and secondary transactions ahead of public listings. These 鈥減re-exit鈥 transactions will not only return capital to investors who have made it through a long liquidity cycle, but also help companies to strengthen their balance sheets ahead of a potential IPO.
At a time when capital is plentiful but selective, the next phase of AI expansion is not just about building breakthrough AI tools. Instead, 2026 is going to be about building AI businesses that can withstand legal, technical, and market scrutiny at scale.
